CN115408900A - Method for optimizing extrusion stress and vibration fatigue life of battery pack system - Google Patents
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Abstract
The invention discloses a method for optimizing extrusion stress and vibration fatigue life of a battery pack system, which comprises the following steps: 1) Setting the thickness of a finite element model part of the battery pack system; 2) Testing system extrusion stress of the finite element model of the battery pack system under different thickness combinations; 3) Testing the system fatigue life of the finite element model of the battery pack system under different thickness combinations; 4) Modifying the thickness of the battery pack system finite element model part, and returning to the step 2) to the step 3), and obtaining the system extrusion stress and the vibration fatigue life of the battery pack system finite element models; 6) Constructing a representation model of extrusion stress and fatigue life; 7) And obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by using a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thicknesses of the components of the battery pack system. The invention solves the problem of multi-objective optimization of the mechanical response of the battery pack system under the extrusion working condition and the vibration working condition.
Description
Technical Field
The invention relates to the field of electric automobiles, in particular to a method for optimizing extrusion stress and vibration fatigue life of a battery pack system.
Background
The battery pack system plays an important role in power supply as a key core component on the electric automobile. Due to the fact that the driving road environment is severe, the traffic environment is increasingly complex, different mechanical conditions (such as vehicle collision, battery pack vibration, obstacle impact and the like) will cause damage which is difficult to estimate to a battery pack system, and in severe cases, safety accidents such as fire disasters and explosions can happen, and therefore the driving safety and the traffic safety of the electric automobile are greatly affected. In addition, if the battery pack system is not extruded and is under the vibration working condition, the reliability of the battery pack system after vibration cannot be evaluated, and potential safety hazards are left for future continuous use of the battery pack and vehicle running.
The battery pack system is a power source of pure electric vehicles and hybrid electric vehicles, and generally comprises a lower bottom shell, an upper cover, a battery module, longitudinal beams/edges, cross beams/edges, module mounting plates, lifting lugs, long/short brackets, reinforcing plates and other components. For a certain configuration of the battery pack system, the safety performance is mainly determined by the thickness and material parameters of the key components. If different battery pack samples are manufactured by changing the thickness parameters of different parts, and experimental analysis is carried out to research the safety of the battery pack samples under the vibration working condition, the time cost and the economic cost are very high. Therefore, the method of combining finite element simulation and deep learning is adopted to predict the vibration stress and the fatigue life of the battery pack system, and the method has very important engineering practical value.
In recent years, related enterprises and universities are dedicated to research on the vibration fatigue safety of different battery pack system components under different thickness parameters, and domestic and foreign experts and scholars also develop systematic research on the vibration fatigue safety of the battery pack system, including methods of optimizing the thickness parameters, adopting novel materials, adopting different battery pack module arrangement modes and the like, but lack of mechanical response evaluation of the battery pack system under various loads.
Disclosure of Invention
The invention aims to provide a method for optimizing the extrusion stress and the vibration fatigue life of a battery pack system, which comprises the following steps:
1) Establishing a finite element model of the battery pack system, and setting the thickness of parts of the finite element model of the battery pack system;
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions;
4) Modifying the thickness of the parts of the finite element model of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different thicknesses of the parts;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model to obtain a representation model of the extrusion stress and the fatigue life;
6) Optimizing the representation model of the extrusion stress and the fatigue life by using a multi-target genetic algorithm to obtain a dual-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
7) And screening out a pareto solution set of the thicknesses of the components of the battery pack system by using a dual-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system.
Further, the step of establishing a finite element model of the battery pack system comprises the following steps:
1) Establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
2) Establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
3) And coupling the shell finite element model and the battery module finite element model according to the connection relation of each component of the battery pack system to obtain the battery pack system finite element model.
Further, the step of establishing a finite element model of the battery module comprises the following steps:
1) Establishing a geometric model of the battery module according to the size parameters of the battery module;
2) Homogenizing the battery module material;
3) And defining material parameters of a geometric model of the battery module according to the battery module material information obtained through homogenization treatment, so as to obtain a finite element model of the battery module.
Further, the thickness of the part comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting support and the thickness of a lower connecting support in a finite element model of the battery pack system.
Further, the vibration working condition comprises a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition.
Further, the step of testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions comprises the following steps:
1) Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the system stress of the battery pack; the vibration working condition parameters comprise a power spectral density curve, vibration frequency and amplitude;
2) Determining the maximum stress amplitude level which can be borne by the finite element model of the battery pack system under the current component thickness according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3) And (3) repeating the steps 1) to 2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
Further, the fatigue life is characterized by the number of stress cycles N at which fatigue failure is reached;
the number of stress cycles N satisfies the following formula:
σ m N＝C (1)
wherein, sigma is the maximum stress, and N is the stress cycle number when the fatigue fracture is reached; and m and C are constants of the battery pack system material.
Further, the third order response surface model is as follows:
in the formula, beta 0 、β i 、β ii 、β ij Representing polynomial coefficients, and rho representing variable numbers; y (x) is an output; x is the number of i 、x j Is an input.
Further, when the representation model of the extrusion stress and the fatigue life is optimized by using the multi-objective genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are optimization targets of the multi-objective genetic algorithm, and the thickness of a finite element model component of the battery pack system is taken as a constraint condition of the multi-objective genetic algorithm.
Further, the step of optimizing the representation model of the extrusion stress and the fatigue life by utilizing the multi-objective genetic algorithm comprises the following steps:
1) Randomly generating an initial solution set population with a set scale by adopting a real number encoding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is one solution of an optimization target;
2) Calculating the fitness and the constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the quality degree of each solution individual according to the fitness and the constraint violation value;
3) Operating the initial solution cluster population through three basic genetic steps of selection, crossing and variation to obtain a filial generation solution cluster population of the initial solution cluster population;
4) Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
5) Merging the parent solution cluster population and the child solution cluster population to obtain a new solution cluster population, calculating the crowding distance of each solution individual according to the spatial position of the objective function value corresponding to each solution individual in the new solution cluster population, and then selecting solution individuals with set scale quantity in the new solution cluster population according to the goodness and badness degree and the crowding distance of each solution individual to generate a new parent solution cluster population;
6) And (5) repeating the steps 3) to 5) until the set maximum iteration number is reached, and finishing the optimization of the representation model of the extrusion stress and the fatigue life.
The technical effects of the invention are undoubted, the NSGA-II evaluation model established by the invention can better evaluate the extrusion stress and the vibration fatigue life of the battery pack system, and an ideal thickness combination of the battery pack system components is screened out, so that the NSGA-II evaluation model can be used for the double-target optimization of the stress and the fatigue life of the battery pack system during extrusion and vibration, and the efficient and low-cost battery pack system design is carried out. In addition, the dual-target optimization method can be used for designing a battery safety early warning system. The method is used for analyzing the influence of various working conditions on the safety of the battery pack system so as to realize the design of the battery pack system with stability and safety. The invention solves the problem of multi-objective optimization of the mechanical response of the battery pack system under the extrusion working condition and the vibration working condition.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of a battery pack system;
in the figure, an upper cover 1, a bottom shell 2, an upper and lower connecting bracket 3, a lower supporting beam 4, a long bracket 5, a short bracket 6, an upper bracket 7, a lifting lug 8, a longitudinal beam 9 and a module mounting plate 10.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and modifications can be made without departing from the technical idea of the invention and the scope of the invention according to the common technical knowledge and the conventional means in the field.
Example 1:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
1) Establishing a finite element model of the battery pack system, and setting the thickness of a component of the finite element model of the battery pack system;
the method for establishing the battery pack system finite element model comprises the following steps:
1.1 Based on the shell size, shell structure and shell material of the battery pack system, establishing a shell finite element model;
1.2 Establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
the method for establishing the finite element model of the battery module comprises the following steps:
1.2.1 According to the size parameters of the battery module, establishing a geometric model of the battery module;
1.2.2 Homogenizing the battery module material;
1.2.3 Defining material parameters of a geometric model of the battery module according to the battery module material information obtained through the homogenization treatment, thereby obtaining a finite element model of the battery module.
1.3 Coupling the shell finite element model and the battery module finite element model according to the connection relation of each component of the battery pack system to obtain the battery pack system finite element model.
The part thickness comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting support and the thickness of a lower connecting support in a finite element model of the battery pack system.
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of a finite element model of the battery pack system under different vibration working conditions;
the vibration working condition comprises a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition.
The method for testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions comprises the following steps:
3.1 Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the system stress of the battery pack; the vibration working condition parameters comprise a power spectral density curve, vibration frequency and amplitude;
3.2 Determining the maximum stress amplitude level which can be borne by the finite element model of the battery pack system under the current component thickness according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3.3 And) repeating the step 3.1) to the step 3.2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
The fatigue life is characterized by the number of stress cycles N at which fatigue failure is reached;
the number of stress cycles N satisfies the following formula:
σ m N＝C (1)
wherein σ is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are constants of the battery pack system material.
4) Modifying the thickness of the parts of the finite element model of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different thicknesses of the parts;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model to obtain a representation model of the extrusion stress and the fatigue life;
the third order response surface model is as follows:
in the formula, beta 0 、β i 、β ii 、β ij Representing polynomial coefficients, and rho representing the number of variables; y (x) is output; x is the number of i 、x j Is an input.
6) Optimizing the representation model of the extrusion stress and the fatigue life by using a multi-objective genetic algorithm to obtain a dual-objective evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
when the multi-target genetic algorithm is used for optimizing the representation model of the extrusion stress and the fatigue life, the extrusion stress and the vibration fatigue life of the battery pack system are optimization targets of the multi-target genetic algorithm, and the thickness of a finite element model component of the battery pack system is taken as a constraint condition of the multi-target genetic algorithm.
The method for optimizing the representation model of the extrusion stress and the fatigue life by utilizing the multi-objective genetic algorithm comprises the following steps of:
6.1 Adopting a real number coding solution mode to randomly generate an initial solution set population with a set scale; the initial solution set population comprises a plurality of solution individuals, and any solution individual is a solution of an optimization target;
6.2 Calculating the fitness and constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the goodness and badness of each solution individual according to the fitness and constraint violation value;
6.3 Operating the initial solution clustering population through three basic genetic steps of selection, crossing and variation to obtain a offspring solution clustering population of the initial solution clustering population;
6.4 Evaluating the goodness and badness of any solution individual in the offspring solution cluster population according to the fitness and the constraint violation value;
6.5 Merging the parent solution cluster population and the child solution cluster population to obtain a new solution cluster population, calculating the crowding distance of each solution individual according to the spatial position of the objective function value corresponding to each solution individual in the new solution cluster population, and then selecting solution individuals with set scale quantity from the new solution cluster population according to the goodness and badness degree of each solution individual and the crowding distance to generate a new parent solution cluster population;
6.6 Step 6.3) to step 6.5) are repeated until the set maximum iteration number is reached, and the optimization of the representation model of the extrusion stress and the fatigue life is completed.
7) A pareto solution set of thicknesses of components of the battery pack system is screened out by using a dual-target evaluation model of extrusion stress and vibration fatigue life of the battery pack system.
Example 2:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
1) Establishing a finite element model of the battery pack system, and setting the thickness of a component of the finite element model of the battery pack system;
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of a finite element model of the battery pack system under different vibration working conditions;
4) Modifying the thickness of the parts of the finite element model of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different thicknesses of the parts;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model to obtain a representation model of the extrusion stress and the fatigue life;
6) Optimizing the representation model of the extrusion stress and the fatigue life by using a multi-target genetic algorithm to obtain a dual-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
7) A pareto solution set of thicknesses of components of the battery pack system is screened out by using a dual-target evaluation model of extrusion stress and vibration fatigue life of the battery pack system.
Example 3:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system mainly comprises the following steps of embodiment 2, wherein the step of establishing a finite element model of the battery pack system comprises the following steps:
1) Establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
2) Establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
3) And coupling the shell finite element model and the battery module finite element model according to the connection relation of each component of the battery pack system to obtain the battery pack system finite element model.
Example 4:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system mainly comprises the following steps of embodiment 3, wherein the step of establishing a finite element model of a battery module comprises the following steps:
1) Establishing a geometric model of the battery module according to the size parameters of the battery module;
2) Homogenizing the battery module material;
3) And defining material parameters of a geometric model of the battery module according to the battery module material information obtained through homogenization treatment, so as to obtain a finite element model of the battery module.
Example 5:
the main steps of the method are shown in embodiment 2, wherein the thicknesses of the components comprise the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting support and the thickness of a lower connecting support in a finite element model of the battery pack system.
Example 6:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system mainly comprises the following steps of embodiment 2, wherein vibration working conditions comprise a random vibration working condition, a positive frequency sweep vibration working condition and a fixed frequency vibration working condition.
Example 7:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system mainly comprises the following steps of embodiment 2, wherein the step of testing the system fatigue life of a finite element model of the battery pack system under different vibration working conditions comprises the following steps:
1) Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the system stress of the battery pack; the vibration working condition parameters comprise a power spectral density curve, vibration frequency and amplitude;
2) Determining the maximum stress amplitude level which can be borne by the finite element model of the battery pack system under the current component thickness according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3) And (3) repeating the steps 1) to 2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
Example 8:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system mainly comprises the following steps of example 2, wherein the fatigue life is represented by the stress cycle number N when the battery pack system is subjected to fatigue fracture;
the stress cycle number N satisfies the following formula:
σ m N＝C (1)
wherein σ is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are constants of the battery pack system material.
Example 9:
the main steps of a method for optimizing the extrusion stress and the vibration fatigue life of a battery pack system are shown in an embodiment 2, wherein the three-order response surface model is as follows:
in the formula, beta 0 、β i 、β ii 、β ij Representing polynomial coefficients, and rho representing the number of variables; y (x) is an output; x is the number of i 、x j Is an input.
Example 10:
the method mainly comprises the steps of embodiment 2, wherein when a representation model of the extrusion stress and the fatigue life is optimized by using a multi-objective genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are optimization targets of the multi-objective genetic algorithm, and the thickness of a finite element model component of the battery pack system is taken as a constraint condition of the multi-objective genetic algorithm.
Example 11:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system mainly comprises the following steps of example 2, wherein the step of optimizing a representation model of the extrusion stress and the fatigue life by utilizing a multi-objective genetic algorithm comprises the following steps:
1) Randomly generating an initial solution set population with a set scale by adopting a real number encoding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is a solution of an optimization target;
2) Calculating the fitness and the constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the quality degree of each solution individual according to the fitness and the constraint violation value;
3) Operating the initial solution cluster population through three basic genetic steps of selection, crossing and variation to obtain a child solution cluster population of the initial solution cluster population;
4) Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
5) Merging the parent solution cluster population and the child solution cluster population to obtain a new solution cluster population, calculating the crowding distance of each solution individual according to the spatial position of the objective function value corresponding to each solution individual in the new solution cluster population, and then selecting solution individuals with set scale quantity in the new solution cluster population according to the goodness and badness degree and the crowding distance of each solution individual to generate a new parent solution cluster population;
6) And (5) repeating the steps 3) to 5) until the set maximum iteration number is reached, and finishing the optimization of the representation model of the extrusion stress and the fatigue life.
Example 12:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
1) And establishing a finite element model of the battery pack system.
The step of establishing the finite element model of the battery pack system comprises the following steps:
1.1 Based on the shell size, shell structure and shell material of the battery pack system, establishing a shell finite element model;
1.2 Establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
the method for establishing the finite element model of the battery module comprises the following steps:
1.2.1 According to the size parameters of the battery module, establishing a geometric model of the battery module;
1.2.2 Homogenizing the battery module material;
1.2.3 Defining material parameters of a geometric model of the battery module according to the battery module material information obtained through the homogenization treatment, thereby obtaining a finite element model of the battery module.
1.3 According to the connection relationship of each component of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
2) Setting the thickness of a finite element model component of the battery pack system; the part thickness comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting support and the thickness of a lower connecting support in a finite element model of the battery pack system.
3) Testing system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
4) The method is used for testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions (the vibration working conditions comprise a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition), and comprises the following steps: defining different power spectral density curves or vibration frequency, amplitude and the like in finite element software, then performing finite element analysis, and acquiring the fatigue life of a finite element model of the battery pack system by using a fatigue life analysis module or special fatigue life analysis software of the software;
5) Modifying the thickness of the parts of the finite element model of the battery pack system, and returning to the steps 3) and 4) until the system extrusion stress and the vibration fatigue life of the finite element models of the battery pack systems are obtained;
6) Establishing a training data set according to the thickness of a battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model, and thus establishing a three-order response surface model to obtain a representation model of the extrusion stress and the fatigue life;
7) And obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by using a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thicknesses of the components of the battery pack system.
Example 13:
referring to fig. 1 to 2, a method for optimizing compressive stress and vibration fatigue life of a battery pack system includes the following steps:
1) And establishing a finite element model of the battery pack system.
The method for establishing the battery pack system finite element model comprises the following steps:
1.1 Based on the shell size, shell structure and shell material of the battery pack system, establishing a shell finite element model; the battery pack system comprises an upper cover 1, a bottom shell 2, an upper connecting support 3, a lower connecting support beam 4, a long bracket 5, a short bracket 6, an upper support 7, a lifting lug 8, a longitudinal beam 9 and a module mounting plate 10.
1.2 Establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
the method for establishing the finite element model of the battery module comprises the following steps:
1.2.1 Establishing a geometric model of the battery module according to the size parameters of the battery module;
1.2.2 Homogenizing the battery module material;
1.2.3 Defining material parameters of a geometric model of the battery module according to the battery module material information obtained through the homogenization treatment, thereby obtaining a finite element model of the battery module.
1.3 According to the connection relationship of each component of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
2) Setting the thickness of a finite element model part of the battery pack system; the part thickness comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting support and the thickness of a lower connecting support in a finite element model of the battery pack system.
3) Testing system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
4) The method for testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions (the vibration working conditions comprise a random vibration working condition, a positive frequency sweep vibration working condition and a fixed frequency vibration working condition) mainly comprises the following steps: defining different power spectral density curves or vibration frequency, amplitude and the like in finite element software, then performing finite element analysis, and acquiring the fatigue life of a finite element model of the battery pack system by using a fatigue life analysis module or special fatigue life analysis software of the software;
5) Modifying the thickness of the battery pack system finite element model part, and returning to the steps 3) and 4) until the system extrusion stress and the vibration fatigue life of the battery pack system finite element models are obtained;
6) Establishing a training data set according to the thickness of a battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model, and thus establishing a three-order response surface model to obtain a representation model of the extrusion stress and the fatigue life;
7) And obtaining a dual-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by using a multi-target genetic algorithm (NSGA-II) and screening a pareto solution set of the thicknesses of the components of the battery pack system.
Example 14:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
s1, establishing a finite element model of a battery pack system;
s2, testing system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
s3, testing the system fatigue life of the finite element model of the battery pack system under different thickness combinations under different vibration working conditions;
s4, modifying the thicknesses of the parts of the finite element model of the battery pack system until system extrusion stress and vibration fatigue life of the finite element models of the battery pack system are obtained;
s5, building a third-order response surface model according to the thickness of the battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model, and obtaining a representation model of the extrusion stress and the fatigue life;
and S6, obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by utilizing a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thicknesses of the components of the battery pack system.
Wherein, the step S1 comprises the following sub-steps:
s11, establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
s12, establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
and S13, coupling the shell finite element model and the battery module finite element model according to the connection relation of all parts of the battery pack system to obtain the battery pack system finite element model.
The beneficial effect of above-mentioned scheme does: according to the invention, the finite element model of the battery pack system is established through the real structural relationship of the battery pack system, and the complete data set is obtained through the finite element model of the battery pack system, so that the obtaining cost of the data set is reduced.
The step S12 includes the following sub-steps:
s121, establishing a geometric model of the battery module according to the size parameters of the battery module;
s122, homogenizing the battery module material;
and S123, defining material parameters of the geometric model of the battery module according to the battery module material information obtained through homogenization treatment, and obtaining a finite element model of the battery module.
The thickness type in the step S3 includes: the thickness of the long bracket, the thickness of the lifting lug, the thickness of the bottom shell, the thickness of the lower supporting beam, the thickness of the upper connecting support and the lower connecting support and the thickness of the upper support.
The step S5 comprises the following sub-steps:
s51, building a third-order response surface model by using the thickness combination of different parts and the extrusion stress of the battery pack system under the combination;
s52, building a three-order response surface model by utilizing thickness combinations of different parts and the vibration fatigue life of the battery pack system under the combination;
when a third-order response surface model is built in the steps S51 and S52, the thickness combination data of different parts are used as input, and the corresponding extrusion stress or vibration fatigue life is used as output.
The beneficial effect of above-mentioned scheme does: a three-order response surface model is built to express a complex mapping relation between combined data with different thicknesses and system extrusion stress and fatigue life, and the implementation process is simple.
Example 15:
as shown in fig. 1, a method for optimizing compressive stress and vibration fatigue life of a battery pack system includes the following steps:
s1, establishing a finite element model of a battery pack system;
in this embodiment, the finite element model can be implemented on different finite element software, for example: LS-DYNA or ABAQUS.
Step S1 includes the following substeps:
s11, establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
in this embodiment, the specific operations of step S11 are: after obtaining the shell size, the shell structure and the shell material, defining parameters such as the type, the size, the thickness and the material of the shell model in finite element software, and establishing the shell finite element model.
S12, establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
the step S12 comprises the following sub-steps:
s121, establishing a geometric model of the battery module according to the size parameters of the battery module;
s122, homogenizing the battery module material;
and S123, defining material parameters of the geometric model of the battery module according to the battery module material information obtained through homogenization treatment, and obtaining a finite element model of the battery module.
And S13, coupling the shell finite element model and the battery module finite element model according to the connection relation of all components of the battery pack system to obtain the battery pack system finite element model.
In step S13, the coupling is a connection relationship between the shell finite element model and the battery module finite element model, and the connection relationship includes: welding, friction, etc.
S2, testing system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
in this embodiment, step S2 specifically includes: on the basis of the requirements of national standard GB38031-2020, according to actual research and development requirements, 120kN extrusion load is selected, extrusion simulation analysis of the battery pack system is carried out, system extrusion stress data of components of the battery pack system under different thickness combination conditions is obtained, and table 1 shows the thickness levels of different components of the battery pack system.
S3, testing system vibration stress and fatigue life of the finite element model of the battery pack system under different thickness combinations under different vibration working conditions;
in this embodiment, step S3 specifically includes: on the basis of the requirements of national standard GB38031-2020, vibration loads in three directions are applied according to actual research and development requirements, vibration simulation analysis of the battery pack system is carried out, and system vibration stress and fatigue life data of battery pack system components under different thickness combination conditions are obtained.
TABLE 1 thickness levels of different parts of the Battery pack System
And S4, modifying the thicknesses of the parts of the finite element model of the battery pack system until the system extrusion stress and the vibration fatigue life of the finite element models of the battery pack system are obtained, wherein the system extrusion stress and the vibration fatigue life of different parts of the battery pack system under the thickness level are shown in a table 2. (ii) a
S5, building a three-order response surface model according to the thickness of the battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model, and obtaining a representation model of the extrusion stress and the fatigue life;
s51, building a third-order response surface model by using the thickness combination of different parts and the extrusion stress of the battery pack system under the combination;
s52, building a three-order response surface model by utilizing thickness combinations of different parts and the vibration fatigue life of the battery pack system under the combination;
when a third-order response surface model is built in the steps S51 and S52, the thickness combination data of different parts are used as input, and the corresponding extrusion stress or vibration fatigue life is used as output.
And S6, obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by utilizing a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thicknesses of the components of the battery pack system.
The experimental results are as follows:
1. the three-order response surface model is built by using the thickness combination of different parts and the extrusion stress of the battery pack system under the combination as follows:
2. the three-order response surface model is built by utilizing the thickness combination of different parts and the vibration fatigue life of the battery pack system under the combination as follows:
3. utilizing a multi-objective genetic algorithm (NSGA-II) to obtain a dual-objective evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system, and screening out pareto solution sets of the thicknesses of components of the battery pack system, wherein 35 groups of screened pareto solution sets are shown in Table 3.
TABLE 2 System extrusion stress and vibration fatigue Life at thickness levels for different parts of the Battery pack System
TABLE 3 NSGA-II selected 35 groups of pareto solutions
In summary, the problem of the optimization method of the extrusion stress and the vibration fatigue life of the battery pack system is comprehensively considered in the embodiment. The result shows that the established NSGA-II evaluation model can better evaluate the extrusion stress and the vibration fatigue life of the battery pack system, an ideal thickness combination of the battery pack system components is screened out, and the battery pack system component can be used for double-target optimization of the stress and the fatigue life of the battery pack system during extrusion and vibration, so that efficient and low-cost battery pack system design is carried out. In addition, the dual-target optimization method can be used for designing a battery safety early warning system. The method is used for analyzing the influence of various working conditions on the safety of the battery pack system so as to realize the design of the battery pack system which is stable and safe.
Claims (10)
1. A method for optimizing extrusion stress and vibration fatigue life of a battery pack system is characterized by comprising the following steps:
1) And establishing a finite element model of the battery pack system, and setting the thickness of parts of the finite element model of the battery pack system.
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions;
4) Modifying the thickness of the battery pack system finite element model part, and repeating the step 2) to the step 3) to obtain the system extrusion stress and the vibration fatigue life of the battery pack system finite element model under different part thicknesses;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a battery pack system finite element model component, the system extrusion stress and the vibration fatigue life of the battery pack system finite element model to obtain a representation model of the extrusion stress and the fatigue life;
6) Optimizing the representation model of the extrusion stress and the fatigue life by using a multi-target genetic algorithm to obtain a dual-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
7) And screening out a pareto solution set of the thicknesses of the components of the battery pack system by using a dual-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system.
2. The method of claim 1, wherein the step of establishing a finite element model of the battery pack system comprises:
1) Establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
2) Establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
3) And coupling the shell finite element model and the battery module finite element model according to the connection relation of each component of the battery pack system to obtain the battery pack system finite element model.
3. The method of claim 2, wherein the step of establishing a finite element model of the battery module comprises:
1) Establishing a geometric model of the battery module according to the size parameters of the battery module;
2) Homogenizing the battery module material;
3) And defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
4. The method of claim 1 for optimizing crush stress and vibration fatigue life of a battery pack system, wherein the method comprises the following steps: the part thickness comprises the thickness of a long bracket in a finite element model of the battery pack system, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting support and the thickness of a lower connecting support.
5. The method of claim 1 for optimizing crush stress and vibration fatigue life of a battery pack system, wherein the method comprises the following steps: the vibration working condition comprises a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition.
6. The method for optimizing extrusion stress and vibration fatigue life of a battery pack system according to claim 1, wherein the step of testing the system fatigue life of the finite element model of the battery pack system under different vibration conditions comprises:
1) Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the system stress of the battery pack; the vibration working condition parameters comprise a power spectral density curve, vibration frequency and amplitude;
2) Determining the maximum stress amplitude level which can be borne by the finite element model of the battery pack system under the current component thickness according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3) And (3) repeating the steps 1) to 2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
7. The method of claim 6, wherein the fatigue life is characterized by the number N of stress cycles to achieve fatigue failure;
the number of stress cycles N satisfies the following formula:
σ m N＝C (1)
wherein σ is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are constants of the battery pack system material.
8. The method of claim 1 for optimizing crush stress and vibration fatigue life of a battery pack system, wherein the method comprises the following steps: the third order response surface model is as follows:
in the formula, beta 0 、β i 、β ii 、β ij Representing a polynomial systemNumber, ρ represents the number of variables; y (x) is an output; x is a radical of a fluorine atom i 、x j Is an input.
9. The method of claim 1 for optimizing crush stress and vibration fatigue life of a battery pack system, wherein the method comprises the following steps: when the representation model of the extrusion stress and the fatigue life is optimized by using the multi-objective genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are optimization targets of the multi-objective genetic algorithm, and the thickness of a finite element model component of the battery pack system is taken as a constraint condition of the multi-objective genetic algorithm.
10. The method as claimed in claim 1, wherein the step of optimizing the representation model of the compressive stress and the fatigue life by using the multi-objective genetic algorithm comprises:
1) Randomly generating an initial solution set population with a set scale by adopting a real number encoding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is one solution of an optimization target;
2) Calculating the fitness and constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the goodness and badness of each solution individual according to the fitness and constraint violation value;
3) Operating the initial solution cluster population through three basic genetic steps of selection, crossing and variation to obtain a filial generation solution cluster population of the initial solution cluster population;
4) Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
5) Merging the parent solution cluster population and the child solution cluster population to obtain a new solution cluster population, calculating the crowding distance of each solution individual according to the spatial position of the objective function value corresponding to each solution individual in the new solution cluster population, and then selecting solution individuals with set scale quantity in the new solution cluster population according to the goodness and badness degree and the crowding distance of each solution individual to generate a new parent solution cluster population;
6) And (5) repeating the steps 3) to 5) until the set maximum iteration number is reached, and finishing the optimization of the representation model of the extrusion stress and the fatigue life.
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